System and methods for inferring intent of website visitors and generating and packaging visitor information for distribution as sales leads or market intelligence

ABSTRACT

A system for inferring intent of visitors to a Website has a visitor-tracking application executing from a digital medium coupled to a server hosting the Website, the server connected to a repository adapted to store data about visitor behavior, and an inference engine for processing the data to infer the intent of visitors. Visitor behavior relative to links is tracked, and intent of a visitor is inferred from one or both, or a combination of analysis of the behavior and deducing meaning for anchor text of links selected.

CROSS-REFERENCE TO RELATED DOCUMENTS

The present application claims priority to provisional patentapplication Ser. Nos. 61/117,098, filed on Nov. 22, 2008, 61/230,691,filed Aug. 1, 2009, and 61/248,546, filed Oct. 5, 2009. Each applicationabove is incorporated in its entirety, at least by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention is in the field of ecommerce and pertainsparticularly to methods and apparatus for inferring intent of Webvisitors and generating leads based on the inferred intent otherinformation about visitors and Website visits.

2. Discussion of the State of the Art

In the field of ecommerce, it is desired that information about visitorsto ecommerce pages be made available to site owners for the purpose ofleveraging information captured about the visitors to the site toincrease sales revenue of the site through proactive contact with thosepotential customers.

It is known that servers track Website visitors by recording theirbrowser behavior at the site especially the sequence of URLs clicked onby the visitor during site navigation. Information about each visitor tothe site is collected where information is available and recorded sothat owners/operators of the site may utilize the information in anattempt to reach potential new customers who have visited the site butwho had not become customers of the site. Tracking cookies are onemethod that is used to track browser navigation and search behavior ofonline visitors in an attempt to determine what the visitors areinterested in.

In many cases custom advertisements in the form of banner ads and othertypes of advertising are pushed to visitor's Internet appliances wherethe visitors exhibited some definitive pattern in Web navigation and/orsome pattern in searching the Internet. An advertiser seeks to deliveradvertising that might be relevant to what the visitor may be interestedin at the time. Gathering data about online activity of persons is oftenused in addition to profile data, survey data, etc. to attempt todetermine what interest patterns exist for the user and therefore whatproducts and services that user might be interested in.

Much of this kind of visitor monitoring and data gathering issemi-automated at best and much manual work is required in order todetermine interests of a user to any degree of authenticity. Moreover,much visitor behavior online may not be simpatico to visitor behaviorsoffline such as store purchase histories, hobbies, work patterns,spending patterns, and general demographics. Another challenge iscategorizing visitors into one or more groups having the same generalproduct or service interest that would serve as a potential marketingbase for a proactive campaign to offer products or services to thosevisitors.

Therefore, what is clearly needed is a system for monitoring andrecording visitor behavior and for inferring intent of visitors to a Webinteractive or Website in a manner that correlates the inference datawith other data gathered using more traditional techniques that wouldresult in better granularity of visitor intent at the site and ingeneral would provide more robust information for group categorizationand generation of better qualified leads.

SUMMARY OF THE INVENTION

In a preferred embodiment of the present invention a system forinferring intent of visitors to a Website is provided, comprising avisitor-tracking application executing from a digital medium coupled toa server hosting the Website, the server connected to a repositoryadapted to store data about visitor behavior, and an inference enginefor processing the data to infer the intent of visitors. In this systemvisitor behavior relative to links is tracked, and intent of a visitoris inferred from one or both, or a combination of analysis of thebehavior and deducing meaning for anchor text of links selected.

In one embodiment the Website consists of one or more Web pages. Also inone embodiment the one or more Web pages include one or more blogs, newsarticles, or ecommerce pages. The visitor behavior recorded may includemouseover or clicking on a link with anchor text. The visitor behaviortracked may also mousing over and clicking on hypertext wherein suchrecorded behavior and time associated therewith is used to fine tune thelevel of visitor intent.

In some embodiments there may be one or more application programinterfaces (APIs) to one or more third-party data-gathering and holdingservices and wherein such data if discovered is used to fine tune levelof intent of the visitor and to identify the visitor without ambiguity.Also in some embodiments one or more of the anchor text instances maypoint to a multimedia presentation, an interactive form, or a datadownload or upload interface.

In some embodiments the system may include a data mining application fornavigating to external data sources and acquiring data from thoseexternal data sources and wherein that data, if discovered, is used tofine tune the level of visitor intent and to identify the visitorwithout ambiguity. In some cases the data miner is enabled to mine datafrom a visitor-subscribed Website using a login token.

Visitor behavior in some cases may include acts of screen capture and orhighlighting or download of text or images. The recorded behavior andthe time associated therewith is used to fine tune the level of visitorintent. In some embodiments the data about one or more visitors andabout visitor behavior including intent is packaged as one or more salesleads presented in a sales information capsule to potential buyers.

In another aspect of the invention a method for inferring intent ofvisitors to a Website includes the steps of (a) tracking visitorbehavior by a visitor-tracking application executing from a digitalmedium coupled to a server hosting the Website, the server connected toa repository adapted to store data about visitor behavior; and (b)inferring intent of visitors from the data by an inference enginededucing meaning of anchor text of links selected by a visitor, and/orvisitor behavior relative to the links.

In one embodiment the Website consists of one or more Web pages. Also inone embodiment the one or more Web pages may include one or more blogs,news articles, or ecommerce pages. Visitor behavior recorded may includemouseover or clicking on a link with anchor text, and such recordedbehavior and time associated therewith is used to fine tune the level ofvisitor intent.

In some embodiments there may be one or more application programinterfaces (APIs) to one or more third-party data-gathering and holdingservices and wherein such data if discovered is used to fine tune levelof intent of the visitor and to identify the visitor without ambiguity.Also in some embodiments one or more of the anchor text instances maypoint to a multimedia presentation, an interactive form, or a datadownload or upload interface.

In some embodiments there may be a data mining application fornavigating to external data sources and acquiring data from thoseexternal data sources and wherein that data, if discovered, is used tofine tune the level of visitor intent and to identify the visitorwithout ambiguity. The data miner may be enabled to mine data from avisitor-subscribed Website using a login token.

In various embodiments visitor behavior include acts of screen captureand or highlighting or download of text or images and wherein thatrecorded behavior and the time associated therewith is used to fine tunethe level of visitor intent. Further, the data about one or morevisitors and about visitor behavior including intent is packaged as oneor more sales leads presented in a sales information capsule topotential buyers.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

FIG. 1 is an architectural overview of a communications networksupporting inference of Web visitor intent according to an embodiment ofthe present invention.

FIG. 2 is a block diagram illustrating components of an inference systemaccording to an embodiment of the present invention.

FIG. 3 is a process flow chart illustrating steps for monitoring a Webvisitor and gathering data about the visitor and visit according to anembodiment of the present invention.

FIG. 4 is a process flow chart illustrating steps for generating leadsand packaging the leads into one or more lead capsules according to anembodiment of the present invention.

FIG. 5 is an exemplary screen shot of a lead capsule interface of aninteractive lead capsule according to an embodiment of the presentinvention.

FIG. 6 is an exemplary screen shot of an additional page of theinteractive lead capsule according to an embodiment of the presentinvention.

FIG. 7 is an interaction sequence chart illustrating tasks forconnecting visitors to agents based on inferred data about the visitorsand the visits to a Website.

FIG. 8 is a process flow diagram illustrating steps for tagging Webpages and generating a tag hierarchy of anchor text phrases according toan embodiment of the present invention.

FIG. 9 is a process flow diagram illustrating steps for monitoring andtracking Web visitor mouse movement relative to anchor text for thepurpose of inferring intent of the visitor according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

The inventors provide in one embodiment a system for inferring theintent of online visitors and for automatically generating leads basedon the inferences wherein such leads may be categorized by similarintent into groups or clusters and wherein such leads may be packagedinteractively for sale to organizations willing to purchase them forproactive engagement purposes. The system and methods of the presentinvention are described in enabling detail using the following examples,which may describe more than one embodiment of the invention.

FIG. 1 is an architectural overview of a communications network 100supporting inference of Web visitor intent according to an embodiment ofthe present invention. Communications network 100 incorporates the wellknown Internet network characterized by the World Wide Web (WWW) andillustrated herein as an Internet backbone 101. Internet backbone 101represents all of the lines, equipment, and access points that make upthe Internet network as a whole. Therefore, there are no geographiclimitations to the practice of the present invention.

Internet 101 is navigable by visitors operating an Internet-capableappliance such as a visitor 105 operating Internet-capable appliance107, which is a personal computer (PC) in this example. Visitor 105navigates Internet 101 using PC 107 running an instance of browserapplication 106. Other Internet-capable appliances may be used tonavigate the Internet such as a smart phone, laptop, personal digitalassistant (PDA) and many 3G cellular telephones. In this example visitor105 utilizes a local Internet Service Provider (ISP) 104 to connect toInternet network 101. Any of several Internet connection schemes may beused by visitor 105 to connect to Internet 101 such as dial-up modem,cable modem, wireless modem, broadband, digital services line (DSL), orthe like. ISP 104 includes a connection server (CS) 107 having access toInternet backbone 101 and a customer database 108 connected to CS 107for storing customer account and contact data. CS 107 is coupled to adigital medium adapted to store data and software required to enableconnection server function.

Contact data held in CDB 108 may include customer name, customeraddress, telephone number, IP address of the customer's primaryInternet-capable appliance used to connect online, email address, andother relevant information. Customer account data may include servicesand products purchased through the ISP and used to enhance the Internetexperience of the customer, credit card information, payment history,user name and password data, and the like. The information held in CDB108 is typically private and is not made available to otherorganizations unless ordered by a court as a result of a search warrantrelevant to a criminal investigation.

A Website hosting service 121 is illustrated in this example andrepresents any organization that hosts Website services for individualsand organizations. Website host 121 includes a Web server 122 is coupledto a digital medium adapted for storing the data and software requiredto enable server function. WS 122 has a plurality of Websites createdand managed for clients like client Website 124. WS 122 has connectionto Internet backbone 101 and to a customer database 123 for storingcustomer contact and account data of customers and organizationscontracting with the Website host for Website hosting services.

A service provider 102 is illustrated in this example and represents acompany or organization that provides the service and system of thepresent invention. Service provider 102 includes a processing dataserver (PDS) 109 coupled to a digital medium adapted to contain all ofthe software and data required to enable server function. PDS 109 hasconnection to Internet backbone 101 and is connected to a lead capsuledatabase 110 that is adapted to store lead data generated by the servicerelative to visitors visiting participating Websites like client Website124, for example.

A buyer or seller organization 103 is illustrated in this example andrepresents either a company or organization willing to sell leadsgenerated from visitor navigation patterns, among other criteria, totheir Website or an organization that wishes to buy leads generated fromWebsite monitoring and lead information development through use of thepresent invention. It is noted herein that in accordance with a serviceenabled by the present invention, such service offering an exchange overwhich users may buy and sell leads, an entity may be a buyer or aseller, or both so long as the buyer account is separate from the selleraccount. In this respect an exchange is provided for sellers and buyersto contract such services from service provider 102. Buyer or sellerorganization 103 may be a business, a company, or an individualoperating from a single account or individual company representatives.In this example, buyer/seller 103 is a company or organization.Buyer/seller 103 has a local area network (LAN) 119 operating thereinthat is connected to Internet backbone 101 through an Internet router(IR) 115. LAN 119 is transfer control protocol/Internet protocol(TCP/IP) enabled.

In one aspect of this embodiment, entity 103 is a buyer that contractswith service provider 102 to purchase sales leads that meet the criteriaof the offered line of products and services available to customersthrough the entity. In one embodiment entity 103, as a buyer, maypurchase leads developed from its' Website and leads developed fromother Websites where those Website owner/operators wish to sell leadsdeveloped from their sites by service provider 102. LAN 119 supports anapplication server (APP) 116 coupled to a digital medium adapted tocontain all of the data and software required to enable server function.APP server 116 is adapted to host one or more enterprise applicationsthat aid in fulfillment of certain goals of the enterprise. In this caseone of the enterprise goals is sales and a well-known application termedsales force application (SFA) 117 is provided on and is executable fromthe digital medium coupled with the APP server. SFA 117 represents aparent/client application that enhances and aids the sales processundertaken by representatives of the organization.

LAN 119 supports a plurality of workstations 120 (1-n). Workstations 120(1-n) include a LAN-connected PC and a telephone in each station.Workstations 120 (1-n) are manned by sales people whose responsibilityit is to generate sales for the entity. An object of the presentinvention is to provide access to developed sales leads to sales peopleoperating workstations 120 (1-n). Each PC in each workstation 120 (1-n)has a sales force client (sfc) displayed in the display screen of eachPC. Each sales force client is a client desktop application of theparent SFA application 117 running on server 116. In a preferredembodiment of the present invention, service provider 102 entertainsentity 103 as a client (lead buyer) for all of the sales peopleassociated with the entity. However, it may be that individual salespeople associated with entity 103 have individual accounts with serviceprovider 102.

Considering entity 103 as a buyer and not a seller, it is important tonote that the software of the present invention enabling purchase of oneor more developed leads may be integrated with SFA 117 running on sever116 in a manner that enables each sales person operating a workstationsrunning a sfc to access filtered leads through a tab added to their sfcvia software download or plug-in. PDS 109 has several SW applicationsinstalled on and executable from the digital medium coupled to theserver. Consider that entity 103 buys leads developed from clientWebsite 124 running on WS 122. In this case, client Website 124 mayrepresent any client Website on the exchange that is willing to sellleads developed from the site.

One unique aspect of the present invention refers to one method ofinferring intent from a Website visitor such as visitor 105. ClientWebsite 124 includes numerous instances of anchor text 126 embedded invarious positions throughout the structure or code of the Website, whichmay incorporate a plurality of separate Web pages and otherinteractives. Anchor text is used as visible text associated with ahyperlink to any other Website interactive such as, but limited to,another Web page, another Website, a download, a flash object, an imageserved, or a multimedia presentation served.

In some embodiments links may be associated to selectable vectorgraphics (SVG), and in these instances any anchor text may not bereadily viewable by a person browsing the web page, but may nonethelessbe retrieved and used in embodiments of the present invention.

Each client Website, in a preferred embodiment, is enhanced with aninstance of Java Script Code (JSC) 125 obtained from service provider102. JSC 125 runs on the Website and is adapted to automaticallyassociate anchor text clicked on by a visitor to the Web interactiveURL/URI that the anchor text invokes. The anchor text becomes a “tag”associated with the URL/URI of the associated interactive and may alsobe associated with any other user tags of the Web interactive includingall of the keywords and phrases that may be extracted from theinteractive. The interactive may be any HTML offering or multimediaoffering that has a URL/URI that may be invoked by clicking on theassociated anchor text.

A data monitor and gatherer 111 is provided on server 109 and is adaptedto monitor visitor activity at a Website and gather any relevant dataabout the visitor and visit to the Website. Monitor 111 tracks, in oneembodiment, the mouse movements of each visitor such as visitor 105 thatvisits client Website 124. Monitor/data gatherer 111 may be installed onthe server that hosts the Website and may report back to serviceprovider 102 to a parent application or directly to an inference engine112, which is adapted to accept the data as input and to develop aninference as to the intent of the Web visitor.

In one embodiment phrases of interest are inferred by recording mousemovements associated with the anchor text wherein the anchor text isclicked on by the user. Monitor/data gatherer 111 may also record searchterms used by the visitor and may develop phrases of interest based onsearch terms used. Monitor/data gatherer 111 may also track instanceswhere the anchor text is hovered over by a visitor but not clicked on.Each Web page of client Website 124 is fully parsed such that all of thephrases, keywords, etc. are recorded for each page. In this way intentmay be inferred by filtering out the keywords, phrases, etc. that arenot interacted with to show, at least what the visitor was notparticularly interested in.

Monitor/data gatherer 111 is adapted to record the time spent on theWebsite and the time spent interacting with any of the anchor textinstances embedded in the Website, which hierarchically speaking arebelow the root anchor text in structure. Hover and click movementsrelative to hypertext (text that links to other text or resources) andanchor text (clickable text in hyperlinks) are both monitored and timedin terms of time spent by the visitor interacting with the structure.Monitor/data gatherer 111, in addition to recording the time spentinteracting with the anchor text, also records the sequence of all ofthe anchor text instances that were interacted with over the wholeWebsite.

As each Website has a hierarchical structure, so too does each anchortext tree beginning with the root text instances followed by anchor textinstances on subsequent pages. For example, a start page may haveseveral instances of anchor text wherein those instances each lead toadditional Web pages and/or resources belonging to the same Website. Soas the Web pages are structured in hierarchies, so too are each of theanchor text instances that are combined with hyperlinks leading to otherresources and so on. Therefore the hierarchical structure of the anchortext tree can be compared to the hierarchical structure of the Website.Hence, phrases of interest (instances of anchor text) are equated totags for each URL/URI that is invoked by an instance of anchor textleading to the page or resource. JSC 125 enables the automatic taggingand creation of the “tag tree” that represents the Website.

All of the above functionality serves to aid the inference engine 112determine the intent of each Web visitor to Website 124. All of the rawdata for each Web visitor that visits Website 124 is fed as raw datainput per visitor to inference engine 112 running on PDS 109. Inferenceengine 112 infers the intent of each visitor based on the phrases andkeywords of interests and the level of intent is determined by theamount of time spent on the keywords and phrases of interest. In oneembodiment inference of intent is enhanced by determining the time spenton each phrase of interest (anchor text) relative to the cumulative timespent on all phrases of interest during the Web visit. History ofvisitor interaction with anchor text is leveraged to determinerepetition patterns relative to interaction with anchor text to helpfine tune the level of intent inferred by the inference engine. Buckets,which are divisions of content in a web page, and inverse page rank areused in some embodiments to calculate intent. These concepts aredescribed in further detail below.

Referring now back to FIG. 1, monitor/data gatherer 111 may gatherinformation about each visitor in addition to monitoring browserbehavior. In one embodiment intent and level of intent of other visitorswith a same or similar profile to a visitor being monitored is used tohelp infer the intent of the instant visitor. Such a profile might bedetermined by applying geo-location techniques on the IP addresses ofthe Web visitors where the profile data consists of zip code, city,state, country, metro code, company name, organization name, and visitoridentity. Visitor identity might be determined from a form fill or bytracking a hyperlink sent to the visitor by email where the visitorclicked on the hyperlink.

Client Website 124 is analyzed to extract all of the content includingthe phrases of anchor text and all other text and images on the site.Each Web page of the site is tagged, as described above with the anchortext (keyword or phrase) that leads to the page. The intent tree is atag tree representing the page hierarchy of the site and the time tospent on the root tag is equal to the time spent on any of the tagsunder the root tags in the structure. In a preferred embodimentmonitor/data gatherer 111 tracks visitor interaction with other elementsof each Web page navigated by the user besides the interaction, andnon-interaction with anchor text on the pages. User mouse movements aretracked relative to highlighting, downloading, screen capture, andhovering over regular text and images on the Web pages. Monitor/datagatherer 111 tracks user activity and attempts to gather additional datawhere possible for each visitor that visits site 124.

Output from inference engine 112 consists of visitor intent, level ofintent, and supporting data that allowed inference of intent of thevisitor. The supporting data is organized as market intelligence datathat accompanies the visitor specific data for each visitor that visitedthe Website. This output may be used by a data mining engine (notillustrated here) to further enhance the quality of each set of visitordata by correlating other information that may be acquired fromthird-party data sources. Such data sources may hold particularinformation about visitors that might not have been available tomonitor/data gatherer 111. For example, if visitor identity and companyaffiliation is not determined during the Web visit, third-partydatabases that contain data about the visitors might be tapped toattempt to infer visitor identity, department, title, company, contactdata, and any other missing element of the visitor profile. The value ofthe visitor information sets for each visitor rises with moreinformation known about the visitor and the visit. In one embodiment theresults of inference of intent of a visitor output by the inferenceengine might be further refined by mining additional data about thevisitor that was not available at the time of the visit.

After all of the visitor data is aggregated for each visitor and intentis inferred including level of intent, the data may be packaged as aworkable lead that may be presented to a buyer on the exchange such asentity 103 in this example. This task may be accomplished with a datapackager and lead generator application 113 resident on and executablefrom the digital medium coupled to PDS 109. Data packager and leadgenerator 113 receives all of the visitor information sets that includethe inferred intent and level of intent for each of the visitors. Theapplication organizes the information according to a presentation modelof an interactive lead capsule that may be priced and presented topotential buyers on the exchange such as buyer 103 of this example. As abuyer, entity 103 or individual account holders comprising sales peopleof the organization may set a variety of constraints or filters thatindicate what types of leads they are willing to purchase over theexchange. Such constraints or filters are provided in the clientapplication or plug-in that integrates the suite of the presentinvention with SFA application 117.

Data packager/lead generator 113 creates individual lead capsules orpackages that may contain any number of leads where the leads packagedtherein may be of varying quality in terms of the data available in eachlead. Leads may be priced on average or priced individually within thecapsule. In one embodiment a buyer may receive a capsule and thenpurchase individual leads or groups or clusters of leads presentedwithin the capsule. A lead-purchase interface may be included withineach lead capsule along with a search engine interface for searching outleads within the capsule by keyword, attribute, price, lead qualityindex, etc. Leads within a capsule may also depreciate with time and maybe reduced in price as the lead ages with time.

A lead capsule database 110 is illustrated in this example and isconnected to PDS 109. Lead capsule database 110 is adapted to storefinished lead capsules, which are interactive navigable files thatinclude several indexed information sections to aid buyers in convenientnavigation of information ad eventual purchase of desired leads withinthe capsule. In one embodiment leads may be extracted in the form of aproactive campaign list where the buyer has purchased a cluster of leadspresented within the lead capsule. The capsule may be enabled togenerate several different types of reports relevant to marketintelligence information.

In one embodiment buyer/seller 103 has a sellers account and sells leadsfrom its Website to potential buyers on the exchange. In this case,Website 124 may be operated and owned by entity 103. In this case theseller 103 may have one or more filters that categorize which types ofvisitors to Website 124 should be included in leads “sold” on theexchange, and which types of visitors the entity may not want to sell asleads. A seller may decide to sell every lead that can be generated fromvisitors to the Website. For example, it is possible that a Website isnot a sales site or commercial business but has a large number ofvisitors that could be interested in subjects, products, services, etc.that the Website educates or otherwise talks about. One example of thismight be a technical blog by a technical author that rates differentvirtual machine packages sold by different companies, for example. Sucha Website might install JSC 125 and have the visitors monitored forintent as they navigate various sections of the blog site.

Such leads then would be valuable to certain virtual machine softwarevendors and might be sold over the exchange to one or more of thosevendors thus creating a steady revenue stream for the blog site inaddition to advertising dollars. One advantage of this is that the blogsite, typically of value to advertisers only if it has a lot ofvisibility, is now valuable if it attracts certain types of individualsthat qualify as visitors having intent towards acquiring a virtualmachine software package in this case.

In one embodiment of the present invention some or all leads within alead capsule may be organized into distinct lead clusters or lead groupswherein the individual leads share one or more common attributes such asintent category and level, company profile, referring Web page, Web pageexited to, income levels, products or services currently owned, and soon. For example, a lead capsule might contain a cluster of leads thatshare intent to purchase a computer and another cluster that share theintent to buy a computer service package for a computer they alreadyown. These two different lead clusters may be packaged within a capsulethat is sold on the exchange to a computer manufacturer that also offersa service package. Such clusters may be organized according to othercriteria like region, similar contact data, or other criteria that maybe identified as one or more filters that might be specified bypotential buyers.

A cluster of leads that share a same geographic location intent onbuying a house might be purchased by a real estate agent where all ofthe leads have an email account and a cell number. The agent may extractthe list of those leads to a compatible, automated email send programthat may be triggered to launch an automated proactive campaign to sendall of those contacts a generic email listing current foreclosures inthe area. A telephone dialer and message system might be used toautomatically call each cell number as well alerting them of theimportant email about new foreclosures in the area. There are unlimitedpossibilities relative to lead generation, lead clustering, and leadextraction for proactive contact. Lead clusters exhibiting certainproperties shared by all of the leads in the cluster may be linked tospecial email templates, telephone messages, SMS templates, and the likefor expedient marketing while the leads are freshest.

In one embodiment, leads may be extracted and proactively engaged byassigning unique call in telephone numbers for the groups, sub-groups,or individual leads where the assigned call-in numbers are mapped tolive sales agent queues or stations in certain sales/service departmentsin the company, or to remote agents working outside the company. In thiscase the numbers to call in may be sent in email, SMS, IM, or any othercommunication method so long as the leads include the appropriatecontact data for receiving those messages. Hence, the messages may bequickly delivered to all leads in a cluster and sales agents may beplaced in position to answer incoming calls from those leads thatrespond to the marketing message.

Referring now back to entity 103 as a buyer, individuals or theorganization as a whole might download one or more lead capsules fromPDS 109 for review. In one embodiment leads that are purchased fromwithin a lead capsule downloaded may be extracted to a lead database 118connected to APP server 116. Such leads may be distributed evenly amongsales stations 120 (1-n) for agents to run their allotments of leads.

It is noted herein that high quality lead may be automatically generatedin near real time from initial visits by visitors such as visitor 105 toclient Websites such as Website 124, however it is a goal of serviceprovider 102 to enhance the quality of those leads over time using datamining techniques to obtain additional information about each visitorsuch as offline activity data, affiliation data, past intent, purchasehistories, lists of products and services purchased in the past, and anyother information that might add value to the lead. Therefore, all ofthe applications involved in the overall process are linked together byextension or API to achieve continuity in the overall process andinference calculations may be automatically repeated for a lead when newdata is obtained about the lead. The inference results may bereclassified for intent and or level of intent. Likewise cluster orgroup associations for leads may be managed such that one or more leadsare shuffled between groups or isolated from a group or added to a groupdepending on subsequent analysis of new or changing data.

It is also noted herein that any new data relevant to leads alreadypurchased may be automatically updated to a lead capsule by refreshingthe lead capsule while online with service provider 102. For example,after a lead capsule is delivered and whether or not leads are purchasedfrom the capsule, one or more of those visitors may log one or more newvisits to Website 124 thus changing the outcome of inference of intentor necessitating lead re-assignment to a cluster or group within thecapsule. If such changes bear on lead price, the pricing of those leadsmay also dynamically change within the capsule to become more valuableor to depreciate in value depending on the result. A 50 cent lead withina delivered capsule might be worth one dollar the day after the capsulewas delivered to a potential buyer so an updating process may be orderedbefore any new lead purchases may be made using a purchasing interfaceprovided within the capsule. In other words a purchase may be allowedonly when online with provider 102 and after an update or refreshprocess is initiated to obtain the most current pricing.

FIG. 2 is a block diagram illustrating components of an inference systemaccording to an embodiment of the present invention. A Websiteillustrated in this example has a start page or root page 201 and one ormore Web pages 202 at least one of which is linked to from the rootpage. Visitors are illustrated herein as visitors 200 (1-n) that aredetected visiting the site at the level of page 201.

Start page 201 contains several instances 203 of anchor text that leadto other pages 202 or to interactive offerings, presentations, or mediaas described further above. Each detected visitor 200 may interact withany one or more or none of the anchor text instances 203. Clicking onone of anchor text instances 203 initiates visitor browser navigation tothe linked resource. Hovering on or right clicking an instance of anchortext 203 reveals information about the resource in an information bubbleor the like. JSC enables recording of the visitor's mouse movements overthe root page including any interaction with any of the anchor textinstances 203.

Monitor/data gatherer 111 tracks the activity of each visitor 200 (1-n).This monitoring activity is logically illustrated herein by a pluralityof visitor monitoring sessions 204 (1-n), each session given, in oneembodiment, a unique session ID for tracking and data sorting purposes.The number of active visitor monitoring sessions is equal to the numberof active visitors navigating the site. Sessions may appear and drop offaccording to the presence of those visitors on the site. There may be aminimum activity threshold or time threshold for a visitor to beclassified as a potential lead. For instance, if a visitor logs on andthen immediately logs off of the Website the data gathered might beignored for that visitor if there is not enough activity or additionaldata to infer intent of that visitor.

Monitor/data gatherer 111 will continue to track each visitor for thetotal amount of time that the visitor is actively navigating theWebsite. This time period lasts from the instant the visitor is detectedand a monitoring session is created for that visitor until the visitorexits or otherwise drops off of the page. If a visitor logs on to thepage and a monitoring session is created but no mouse movements ornavigation is detected for a threshold period of time, that visitorsession may be terminated and the visitor may be passed over for intentinference. After monitor/data gatherer 111 detect that the visitor hasexited the site, the application attempts to determine which page if anythe visitor exited to. A link to an exit page might contain anchor textthat described the exit page. In one embodiment a tracking cookie canprovide the URL of the exit page and data mining may later determine thecontent of the page exited to from the site.

After a monitoring session is determined complete for a Web visit for avisitor (from site log on to site exit), the raw data about the visitorand the visit in passed to inference engine 112 for that visitor. Theunique session ID assigned to the monitoring session may be retained toserver as a unique identifier for the data relevant to the session andvisitor. Inference engine 112 accepts the data input and primarily mapsthe visitor activity relevant to the anchor text interactions thatoccurred during the session to a hierarchical tag tree representing theWebsite structure in terms of the locations of each anchor text instanceinteracted with on the site. Inference engine 112 may run severaladditional algorithms that are designed to help with determination ofintent of the visitor at least during the Website monitoring session ofthe visitor.

Determining and Quantifying Visitor Intent

In practice of the invention, the intent of a visit can be determinedbased on . . . .

(1) The search terms used in the referrer website (such as searchengine) prior to landing on the root page.(2) The set of tags associated with each page visited in that session.

The tags as mentioned previously are determined based on the anchor textassociated with certain links. While the tags determine the intent ofthe user, they do not help in automatic classification between a“curious” visitor and an “interested” visitor (level of intent). Onefactor which differentiates the two users is the time spent on thewebpage, since any page requires the user to spend a certain amount oftime in reading and assimilating the content on it. The Intent of thevisitor in a particular session is determined by a set of tuples, eachcomprising a tag and the time spent on a page with that tag, for all thepages visited in the Web session. The time spent can either be the timespent on the page or could be made more accurate by determining the“useful” time spent on the page.

The useful time spent on the page can be determined based on activitycarried out on the page (which will eliminate false positives which mayresult on account of the page being kept open in a browser). Yet anotherdifferentiating factor between these two types of visitors is the“single-mindedness” of intent. A curious visitor, contrary to theinterested visitor, does not typically show single-mindedness and doesnot seek in-depth knowledge. Unless the specific page visited is reacheddirectly from a referrer website, the visitor goes through a series oflinks, before hitting upon the desired page. The deeper the usertraverses in the graph, greater the focus of the visit. The deeper pagesin the web site hierarchy have a lower page rank (computed on thewebsite graph and not the globally published page rank, by Google™Inc.). Therefore, the reciprocal of the page rank indicates a higherinterest level.

In actual practice, the visitor's intent can be quantified by a function

$h\left( {{h_{1}\left( {{time}\mspace{14mu} {spent}} \right)},{h_{2}\left( \frac{1}{{Page}^{\prime}s\mspace{14mu} {Rank}} \right)}} \right)$

where h∝h₁(time spent) and

$h \propto {h_{2}\left( \frac{1}{{Page}^{\prime}s\mspace{14mu} {Rank}} \right)}$

h₁ can take any form which shows direct proportionality viz. linearfunction or a step function, etc. Similarly h₂ can take any form whichcaptures the inverse proportionality with h. The above score computedbased on the time and page rank is referred to by the inventor as a“Gauge Score” or G-score.

During any visit, since the visitor will (in all probability) traversemore than one link of the Website, there is a need to order those pages.The G-score is computed for all of the pages visited. The pages can besorted based on the G-score computed for all the pages. Further, asingle intent can be determined by removing all other nodes, which arethe predecessors of the node with the highest score in the spanning treeT (which is the tag tree—the construction of which is explainedsubsequently.). However, the deepest node in the hierarchy may notalways clearly represent the intent of the user. For example, a personvisits a Web page pertaining to “Macbooks™” and then the deepest visitedmay be page pertaining to the specification of the Macbook™.

While the specifications page may be at the deepest level and hence hasthe highest inverse page rank, it does not capture the intent—Macbook.So in such cases, the immediate parent node needs to be considered asthe primary intent. This can be determined using either the searchkeyword(s) obtained from the referrer site or from the list of nodesvisited during that session. Further, the ordering of the results can beused to get customized views by filtering the nodes by the bucket towhich they belong. Buckets are sections into which a website can bedivided based on the intent of the visitor, for example, Products,Solutions, and Services, all sections that relate to items that acompany might sell. The bucket under which a node lies is determinedfrom the spanning tree. Alternately, The G-Score is further enhanced byusing the bucket weight of the node, w(h, b), where w is the weightfunction, h is the G-score and b is the bucket weight. Each bucketweight is assigned a weight based on its relative importance. If thenode has been classified incorrectly by the spanning tree algorithm, itmay be reassigned to a different subtree based on manual input.Furthermore, the tag and the G-score for each session from the sameorganization may be aggregated to obtain an overall score for theorganization's interest relative to an intent classification. Moreover,the overall G-score of each visit may be additionally weighted based onthe designation of the visitor. The higher the designation the greateris the weight.

In summary of the above description, determination of intent of thevisitor may be inferred from search string of referrer or from the tagsof the pages visited. The tags, in preferred embodiments of theinvention, are obtained from the anchor text of the link. Visitors aresorted automatically based on intent quantification (intent level)computed using any of or all of the following . . . .

(a) Time spent including “useful” time spent on the page.(b) The depth of the page in the website hierarchy. It is noted hereinthat the “depth” of page refers to a method to indicate itsaccessibility from the root. The depth of a tree can be measured viaseveral methods, one of which includes the page rank computed on thegraph.(c) Ordering the pages visited based on G-Score.(d) Using the tags of the predecessor nodes in the spanning tree toqualify the intent with the intent of avoiding potentially ambiguousintents.(e) Displaying customized views of visits based on the buckets to whichthe nodes belong.(f) Allowing manual movement of nodes from one subtree to anothersubtree of the spanning tree of the graph. This flexibility is orderedin case the heuristic generating the spanning tree incorrectlyassociates a node to a wrong subtree.(g) Aggregating the G-Scores across several visits from the sameorganization to present organization based results where the visit froma certain organization is determined based on IP address, etc.(h) Weighting the G-score with designation of the visitor, where thedesignation of the visitor is determined using form fill, etc.

It is noted herein that the above sequence may represent one or moremethods for determining accurate intent of a visitor to a monitoredWebsite wherein the steps thus disclosed above may be followed in or outof order of listing and wherein some or all of the steps are practicedin such a method, and which method shall not require illustrativesupport if claimed herein in this specification.

With reference to activity interacting with anchor text, the inferenceengine takes into account the instances of anchor text 203 hovered on orclicked on by the visitor, where the anchor can be a text hyperlink or amultimedia hyperlink. Examples of multimedia hyperlinks may be an imagehyperlink or a flash object hyperlink.

The inference engine, in the case of a text hyperlink, may infer intentof a visitor in a number of ways. In the case of anchor text, the textstrings themselves may be analyzed semantically, using a variety ofinformation sources, some of which may be local, and others of which maybe remote. It is clear that a visitor, interacting with anchor text, isin high probability motivated (his or her intent) by the meaning of thewords of the text. So an important part of determining the intent of thevisitor is in the meaning of the text, words in the text, or relatedwords and phrases to the text. In some cases the intent may be deducedjust by this semantic means alone. In some cases the inference may alsotake into account syntax, that is, the placement and the way that wordsmay be used in a phrase. Further, inference may be made by the behaviorof a visitor relative to anchor text, such as the number of times andthe frequency with which the visitor might return to a particularanchor, time hovering over a particular text without invoking the link,and so on. So inference in embodiments of the invention may be throughany one of, or any combination of semantic, syntactic or statisticalmeans.

The inference engine in some embodiments, as mentioned just above, takesinto account the length of time that the visitor hovered on the anchortext. The inference engine also takes into account the length of timespent by the visitor on the page resulting from clicking on thehyperlink associated with the anchor text to navigate to one of pages202. This is the URL that the visitor navigates to by clicking on thehyperlink associated with the anchor text. The inference engine furtheraccounts for the number of times that the visitor hovered on or clickedon each anchor text and the sequence or order of hovering on or clickingon the anchor text.

With respect to other text on the Website besides anchor text, theinference engine receives data from monitor/data gatherer 111 relativeto visitor activity with those instances of text. For example themonitor/data gatherer observes and records the behavior of the visitorrelated to highlighting of text on the Website including root page 201and any subsequent pages 202. The inference engine may take into accountthe text highlighted by the visitor using the mouse or the keyboard. Theinference engine may take into account the length of time the text washighlighted including the length of time spent by the mouse pointer onand around the highlighted text. The inference engine accounts for thenumber of times the text was highlighted and the sequence or order ofhighlighting the text during the visit.

The output of inference engine 112 may be input into data packager/leadgenerator 113 described and illustrated with reference to FIG. 1. Theoutput of data packager/lead generator 113 is logically assumed to bequalified leads that may be included in a lead capsule. The generatedleads include all of the information about the visit and visitorincluding all of the phrases and keywords of interest along with aclassified intent and a level of the intensity of the intent of thevisitor. A data mining/lead clustering engine 205 may be provided forthe purpose of developing the quality of leads further and to clusterleads based on one or more shared attributes like the same level of aclassified intent and same geographic region, or same intentclassification and same company affiliation, etc.

As described further above, every Website has a tree structure. The homepage is the root and the anchor text on the home page would be the firstbranch level nodes of the tag hierarchy, for example products,solutions, careers, and so on. The structure of the Website isthoroughly analyzed and all the subsequent Web pages 202 are tagged withthe anchor text leading to those pages. In this way the tags reveal thebase intent of the visitor. The hierarchy of Web pages translates to ahierarchy of “tags”. The amount of time spent by a visitor on a “tag” isequal to the amount of time spent on all the “tags” in the sub treeunder that “tag”. Intent of a visitor is a function of a few basicparameters, those being, the tags associated with the visit, the timeassociated with each tag, and the path traversed by the visitor in thetree.

In this example data mining/clustering engine 205 looks for additionaldata that can be added to leads to boost the value of those leads andgroups certain leads together via a clustering application to form leadclusters containing a plurality of individual leads that are alike insome important way from the perspective of the lead buyer. A buyer maycreate filters that pertain to lead clustering in addition to filtersfor screening out leads that the buyer is not interested in.

It is noted herein that data mining and lead clustering may be separateapplications without departing from the spirit and scope of the presentinvention. Moreover, all of the applications thus far described might beprovided as one integrated application or multiple separate applicationssome of which may be distributed to other machines. In one embodimentdata mining and lead clustering occurs before data packaging and leadgeneration where the lead capsule is concerned.

In this example the leads generated are grouped one by one into clustersCL-1 and CL-2 for a potential buyer. Proactive contact may be bytelephone or by any other contact data provided with each lead. In thisexample the buyer may have specified that all of the leads within leadcluster 1 and within lead cluster 2 have email accounts and a workingemail address. Clusters may be managed on a periodic basis. In thisembodiment there is a plurality of pre-established email templates 206(1-n). Each email templates 206 (1-n) may be maintained by the buyer oflead clusters CL-1 and CL-2. The clustering engine can generate leadclusters under specific parameters such that one of the predesignedemail templates specifically solicits the leads in a cluster based onthe intent of the leads. In one embodiment the service provider mayprovide templates that are based on the intent specific to products orservices and matches the best template to a lead cluster such that whenthe leads are extracted from the cluster into an email list, thetemplate automatically executes as the email that will be sent to all ofthose leads proactively.

FIG. 3 is a process flow chart illustrating steps 300 for monitoring aWeb visitor and gathering data about the visitor and visit according toan embodiment of the present invention. At step 301, a visitor isdetected on a Website adapted for visitor monitoring. At step 302 thevisitor is monitored for activity. At step 303 the monitoring system maydetermine if there is any previous page data available. An example mightbe that the visitor entered the site from a referral page. The datagatherer application might have access to this data through a trackingcookie or through browser history data.

If there is data available about a previous page from whence the visitorcame onto the Website at step 303, then at step 304 the monitoring/datagathering system determines if it was a standard Web page from anotherWebsite. If the system determined that it was a standard Web page, thenat step 305 the data gatherer captures the URL and title of thatprevious Web page the visitor was on before entering the Website andbeing subject to monitoring. If at step 303 the system determined thatthere was previous page data available and at step 304 the systemdetermined that it was not a standard Web page, the system determines atstep 306 if the previous data was from a search result page. If thesystem determined that the previous page was a search engine resultspage, then the data gatherer captures the URL and search terms used inthe search that listed the current Website in the results.

If at step 303 there was no previous page data, the system determines ifthe visitor is identified without ambiguity at step 308. If the visitoridentification is available at step 308, the data gatherer captures thevisitor name and contact information if available at step 309. Back atstep 306 if the page data cannot be identified as a Web page or searchresults page the process may move directly to step 308. If at step 308the system cannot identify the visitor without ambiguity, the datagatherer captures IP address, title of the visitor, and any otheravailable data about the visitor that might help later to obtain thename of the visitor at step 310.

At step 311 the monitor application tracks all of the movements of thevisitor throughout the Website. The monitor also captures all of thepage transitions from one Web page of the Website to another Web page ofthe Website at step 311. The movements tracked include all of the mouseand keyboard actions perpetrated by the visitor while at the Website.The page transitions amount to the anchor text “tags” leading to thosepages. In steps 311 and 312, the system also records the time spent ineach interaction and the total time spent on each page of the Website.

At step 313 the system determines if there is any exit page dataavailable. The exit page data referred to would be data from a Web pageexited to from the Website wherein the page is not formally part of theWebsite and wherein the page is navigated to by clicking on anchor textin a page of the current Website. An exit page is tagged by an anchortext directing the visitor to that page. Therefore exit page data isavailable if the visitor exits the Website to that page referenced inthe anchor text associated with the hyperlink to the page. If there isexit page data at step 313 the system captures the URL and title of thepage at step 314. The process then ends for that visitor at step 315. Ifthere is no exit page data available at step 313 them the process endsat step 315 for that visitor. In this example, much of the data capturedat steps 305, 307, 309, and 310 may be used as input to search othersources for more information about the visitor. For example if thevisitor cannot be identified without ambiguity at step 308 then the datacaptured at step 310 will later be used to try to determine the visitorsidentity through other methods including offline research if necessary.

FIG. 4 is a process flow chart illustrating steps 400 for generatingleads and packaging the leads into one or more lead capsules accordingto an embodiment of the present invention. Steps 400 illustrate aprocess that may be an extension of the process of FIG. 3 above. At step401 the raw data sets and data found during look up operations ifperformed is sent to an inference engine analogous to the inferenceengine of FIG. 2 to determine intent of each visitor. At step 402 theinference engine processes each data to infer intent of the visitors.This step is performed after monitoring and data capture tasks arecompleted for the visitors. In one aspect the inference engine may beenabled to determine if visitor data sets input into the engine arethose of new and fresh visitors or customers who have come back to theWebsite after already being processed as a lead. At step 403 the systemmakes a determination of whether there are any new visitors to the site.

If there were no new or fresh visitors detected in step 403, the systemmay update a lead database (LDB) at step 404 with any new datadiscovered about the existing leads. Such data may affect the intentclassification or intent level of a lead, the cluster assignment for thelead or other attributes of the lead such as pricing for the lead. If atstep 403 there are fresh visitors detected, the lead generator generatesnew leads from those visitor data sets at step 405. At step 406 thesenew leads may be correlated with one another to form one or more newlead clusters at step 407 based on shared attributes of the generatedleads. In one aspect the new leads are added to existing lead clustersbased on similar attributes rather than forming many new lead clusters.Having too many narrowly defined lead clusters is not desirable. Forminga few lead clusters that remain relevant to the proactive designs of abuyer enterprise is more desirable.

At step 408 the system continues to manage existing lead clusters andthe process moves to step 404 where the lead database is updated. Atstep 409 one or more new lead capsules may be created. At step 410 thesystem may set initial pricing for the newer leads. At step 411, thoselead packages are offered over the Internet on the lead exchange.

In one embodiment old leads are updated with new information if possibleas a result of a recent visit or visits to monitored Websites. In oneembodiment after a threshold period of time, old leads may bere-packaged as new leads if intent for other services products, etc isdiscovered. A visitor may frequent a number of different Websites thatare monitored by the service provider making leads available on theexchange. In this regard a same visitor may be packaged as a number ofdifferent leads having different intent classifications based on thecontent visited and thus the visitor data may be purchased as a lead bywidely different buyers whose products and services matched the intentof the lead.

FIG. 5 is an exemplary screen shot of a lead capsule interface 500 of aninteractive lead capsule according to an embodiment of the presentinvention. Interface 500 is a browser-based interface used for reviewinga lead capsule and for purchasing leads presented within the leadcapsule. Interface 500 includes browser tabs 501, a scroll bar 502, anda navigation field 503. Familiar browser options menus and icons may beassumed present in interface 500. A lead capsule may be navigated by anybrowser application and all of the pages within a lead capsule may behypertext markup language (HTML) pages or of a similar markup. Leadcapsules may be reviewed remotely by potential buyers or they may bedownloaded and reviewed offline. In a preferred embodiment some of theattributes of leads such as contact data and the like are not visibleuntil the lead has been purchased.

Within the workspace of interface 500 a welcome statement and logoutoption 504 is illustrated. A lead capsule may require authentication forreview and purchase of leads. In this example a potential buyer may berequired to become a registered member of the lead exchange and may berequired to authenticate in order to review a lead capsule. A set ofpurchasing options 505 is presented for potential buyers to buy leadsfrom within the capsule. In one embodiment a potential buyer may accessany lead capsule and purchase leads if the capsule is not exclusivelyowned by another buyer. In one embodiment a buyer may purchase some orall of the leads within a lead capsule at a price that is an exclusiveprice in which case the capsule would not be available to others. Inanother embodiment leads within a lead capsule may be purchased by morethan one buyer at a non-exclusive price. There may be discounts givenfor numbers of leads purchased.

Options 505 include a leads button that enables the potential buyer tobrowse leads for purchase. An account button enables the user to viewhis or her account status. An alert button enables a user to set alertswhen leads of a particular category or type become available on theexchange. An interface is available for the buyer to add credit to hisor her account for purchasing leads. Payment options may include pre-payoptions, credit card options, PayPal™, and online check. A potentialbuyer may put money in a special account that is drawn upon when leadsare purchased through the interface. There may be a minimum balance orpurchase amount that is imposed on buyers that purchase leads throughthe interface.

A lead presentation window 506 is illustrated in this example and isadapted to list interactive leads as search results. A lead searchinterface 509 is provided as a dropdown menu containing a list of searchoptions for searching for leads within the lead capsule. A keywordhypertext listing pointing to a lead will be descriptive of the intentof the visitor of the lead. A page listing interface 508 shows thenumber of pages of leads available in the capsule that match the searchcriteria input into search interface 509. Any listed lead within window506 may be reviewed by clicking on the lead anchor text and reviewingthe lead data in a separate window. A lead that is not purchased willhave some blank fields such as blank contact data fields that will bevisible to the buyer only after the lead has been purchased. In oneembodiment lead groups or clusters may be listed by keyword, phrase, orother search term. A potential buyer may click on a group to expand thelist of leads belonging to that group. A group of leads may be purchasedby double clicking the lead group or cluster keyword, which shouldreveal the intent of all of the visitors of the leads for that group.

A second lead presentation window 507 lists all of the leads in thecapsule by the phrases of interest for each lead. Leads may be listed bycommon phrase of interest in a search. Leads may be listed by region,country, city, company affiliation, and many other lead parameters. Inwindow 507 leads are listed by the phrases of interest for those leads.Each phrase of interest speaks to the intent of the visitor of the lead.In one embodiment the time spent on each phrase of interest is listedalong with the phrase using anchor text that points to the lead and allof the lead data available. In one embodiment leads may be listed byprice range. Higher priced leads may be listed first followed by leadsof mid-range pricing, followed by low priced leads. Leads may be sortedby the date of generation. Each lead may have a unique ID number soleads may be tracked and updated. Each lead listed in windows 506 and507 can be interacted with to view lead data associated with the lead. Alead presented within interface 500 may have associated with it much ofthe raw data that was used to classify intent, and price the lead. Thisraw data may be organized into sections for each lead.

FIG. 6 is an exemplary screen shot of an additional page 600 of theinteractive lead capsule according to an embodiment of the presentinvention. Browser-based page 600 includes a window 601 listing leads bythe phrases of interest captured for each lead. A window 602 isillustrated that lists leads by the pages of interest to the visitors ofthe leads when they were most recently navigating the Website. A page orpages of interest may be a Web page of a Website that the visitor spentmore time on in comparison with other pages of the site. It is notedherein that the total time spent on a phrase of interest or page ofinterest may be included in the listing.

Page 600 includes several lead data viewing options. A viewing option603 is provided for viewing visitor history including browsing history,purchasing history, and other history data discovered about the visitor.This information may be non-accessible until the lead has beenpurchased. A viewing option 604 enables a potential buyer to viewinformation associated with a referring page or article that the visitorviewed or read that referred the visitor to the Website from which thelead was generated. This information may also include URL, title,keywords, search terms, text content, download and/or uploadinformation, and anchor text clicked on that directed the visitor to theWebsite from which the lead was generated.

A viewing option 605 is provided for the potential buyer to view visitorinformation, which may include visitor company affiliation, visitorprofiles, visitor online associations, and any other incidentalinformation about the visitor that was discovered. Some or all of thisinformation may be withheld until the lead has been purchased. Forexample, contact data would not be available unless the lead waspurchased. Another viewing option 606 enables a buyer who has purchasedthe lead to view the contact information of the visitor. In oneembodiment further options are provided for viewing lead clusterstatistics and information such as what shared attributes compelled thesystem to form the cluster. Ideally, clustering is performed toaggregate like leads for a buyer that has indicated criteria (filter(s))for purchasing leads. A lead capsule may be generated that containsleads developed from many different Websites monitored by the serviceprovider. Lead clustering may be performed based on knowledge frompotential lead buyers that are looking for particular classifications ofintent.

A lead capsule may contain many leads of different types generated atdifferent times over a plurality of monitored Websites. On the otherhand, a lead capsule may contain leads that were generated from a singlemonitored Website. Buyers of lead capsules may be allowed to share leadscontained within the lead capsules. Clusters or groups of related leadsmay be presented within a lead capsule. Lead capsules may also beclustered based on shared attributes of the leads contained within eachlead capsule.

FIG. 7 is an interaction sequence chart illustrating tasks forconnecting visitors to agents based on inferred data about the visitorsand the visits to a Website. Visitors are detected on a monitoredWebsite with JSC installed to enable visitor monitoring and datagathering. A monitor/data gatherer monitors visitor activity for eachvisitor and gathers data where available about each visitor. Eachvisitor is monitored from time of detection of the visitor on the siteuntil the time the visitor leaves the site. The monitor/data gathererrecords the visitor activity related to anchor text on the Website, timespent on pages of the Website and time spent interacting with portionsof the Website. The sequence of interaction with anchor text instancesis recorded for each visitor. The IP address information is logged foreach visitor. The time and date of each visit is recorded for eachvisitor, and a tracking cookie may be sent to each visitor appliance sothat activity away from the site might be discovered during subsequentvisits to the site. Other types of visitor activities in addition to thetypes listed herein may be performed at the site such as text or imagedownload or upload content monitoring, screen capture activitymonitoring, site search activity monitoring, form fill activitymonitoring, and tag creation activity monitoring.

The monitor/data gatherer forwards the raw data per visitor to aninference engine adapted to infer the intent of each visitor based onthe activity data, especially activity relative to interaction withanchor text on the site. When the inference engine receive the raw datainput is analyzes the input to determine intent of the visitor andnon-intent of the visitor. The data gatherer attempts to gather dataabout the visitor like the company or organization the visitor isaffiliated with and the geo-location of the visitor. The data gatherermay gather identification information about the visitor, the contactinformation of the visitor, and any other information that is availableto the gatherer during the visit.

The output of the inference engine includes intent of the visitor,phrases, buckets and keywords of interest to the visitor and all of theassociated data recorded that was used by the inference engine to helpclassify the intent of the visitor and the level of intent wherepossible. Some information may not be available at the time that avisitor visits the Website. Therefore, all of the previous data and theoutput from the inference engine are forwarded to a lead generationengine adapted to generate leads and to group leads into clusters basedon shared attributes. The lead generation process may include datamining and further lead development including updated intentclassification or intent level.

A data mining engine may utilize any of the data gathered during thevisit by the visitor to lookup additional data from external andthird-party data sources. For example, the data miner may attempt todiscover ID (identification without ambiguity) or at least narrow thevisitor's possible ID to a few possible contacts for which the contactdata would be provided (identification with ambiguity). The leadgeneration engine is responsible for packaging lead data into a leadcapsule or sales information capsule. In this regard the visitors areidentified without ambiguity if possible. If not possible then visitorsare identified with ambiguity meaning that the identification may pointto more than one possible contact such as one of a number of personsthat belong to a group that was identified without ambiguity.

Visitors may be profiled and clustered based on shared attributes. Forexample, a cluster or group of visitors may be a group sharing a sameintent classification. A cluster or group of visitors may be those whohave a same company affiliation. In one embodiment the visitor or leadclusters are mapped to an appropriate pre-designed proactive emailtemplate. For example, if a cluster of leads shares the attribute intentto buy a computer, then that cluster would be mapped to an emailtemplate that offers the visitor a computer as opposed to one that mightoffer the customer a server. A proactive campaign using email may belaunched automatically at the moment a cluster is associated with thecorrect email template.

After generating leads and further developing the quality of thoseleads, high level lead data may be forwarded to a telephone connectionengine. In this case call-in telephone numbers may be assigned to everylead in a cluster of leads and those telephone numbers may be sent outto those visitors via the pre-designed email template that solicitsthose potential customers to call in to get a discount on a product orservice, for example. The connection engine may map the assigned call-innumbers to live agent extensions in a manner that would distributeworkload evenly during an influx of generated inbound calls for a salegroup that is much smaller than the amount of leads in the cluster.

All of the steps described above may be performed without manual humanintervention during the process. In another embodiment a lead clustercan be extracted to the form of an outbound calling list where a machineautomatically calls each lead in the cluster and makes an offer to thecustomers that answer the calls. Accepted offers are then routed toavailable personnel. The system may also forward the lead data includingintent and intent level to any live operator that is talking a call fromthe subject of the lead. If a lead is identified with ambiguity andseveral contacts are associated with the lead as possible subjects ofthe lead then all of those contacts may be solicited proactively wherethe contact data is available.

In this example, leads are generated and packaged for distribution withautomated proactive means for contacting those leads included in thepackaging. In other embodiments buyers make their own decisions abouthow best to process the leads they purchase. It is noted herein thatleads may be continually updated wherein new lead data discovered may beadded to the lead and obsolete lead data may be purged. Leads may bereclassified for intent, level of intent, and they may be reassigned toa different cluster or to no cluster depending on latest informationavailable about the subject to the lead.

For example, consider a visitor visited a Website 1 week ago andinferred intent was that the visitor wanted a packaging service forproducts the visitor is manufacturing. Now consider that the samevisitor visits another monitored Website looking for packaging materialsfor packaging products for shipment. The latter intent may serve asevidence that the visitor decided to personally package the productswithout using a packaging service. If this lead from one week ago islisted in a cluster of leads in a lead capsule, refresh of the capsulewhile connected to the service provider may result in update of newinformation about the lead, which will change the intent classificationof the lead and would change the cluster assignment of the lead if theshared parameter was “looking for a packaging service”. Pricing of thelead may drop if the refresh occurs in a capsule that is owned by acompany that only sells the packaging service but not the packaging.

FIG. 8 is a process flow diagram illustrating steps 800 for tagging Webpages and generating a tag hierarchy of anchor text phrases according toan embodiment of the present invention. At step 801 the system(automated), or a knowledge worker (manual) accesses a monitored Websitestructure for analysis. At step 802 the system identifies the root nodeor the main start page of the Website. At step 803 the system identifiesthe node (page) hierarchy of the Website starting with the root page ondown the tree. At step 804 the system determines if the entire treestructure is identified. If the analysis is not complete at step 804,then the process may loop back to step 802 until all of the nodes in thetree are correctly identified in the tree structure.

When the entire Web tree (hierarchy) is correctly identified it may bedisplayed at step 805. At step 806 the system identifies each instanceof node transition anchor text in the tree structure. The transitionalanchor text is the anchor text visible to the user associated with thehyperlinks located in the first node and subsequent nodes on down thetree that lead to other nodes down the tree structure from the beginningnode. It is noted herein that anchor text may also lead to a multimediapresentation, an image, a form, or some other Web interactive supportedby browser navigation. That is to say that each node is a resourcelocated by executing hyperlink containing a URL and/or a URI. Thevisible part of the hyperlink is the anchor text that is a keyword orphrase.

At step 807 the system validates the work by re-checking to see if theidentified hyperlinks share the same tree structure as the identifiednodes in the Web structure. For example, the instances of anchor textlocated in the start page or beginning node of the Web structure are allfirst level nodes in a top branch, all of them leading to one or moresecond level resources. In an example consider the anchor text instances“products”, “solutions”, “careers”, “partners”, “about us”, “sign up fornew product alerts”, “watch a demo”, “download drivers”, etc. to bekeywords and phrases of user intent. These keywords and phrases are usedas tags to tag the resources they lead to in the tree.

If the hierarchy of the tags matches the hierarchy of the Website atstep 807, at step 808 the system tags each transitional node or resourcewith the anchor text that leads to visitor's browser to that node orresource. It is noted herein that there may be more than one hyperlinkcontaining anchor text that leads to a same second level node (Webpageor resource). In this case the second level node is tagged with all ofthe anchor text instances that lead to it. The system automatically tagsall of the linked resources including pages and other interactivethroughout the structure creating a tag tree where the tags describe thenodes that they lead to. At step 809 the system may display the tagtree.

At step 810 the system may again validate that the Web tree hierarchyand tag tree hierarchy matches. If the two hierarchies do not match withreference to the mapping, then the process may loop back to step 808 tomake sure each node was properly tagged looking for any possible errorsmade during the process. It is important that leads not be created wherethe visitor's intent is misidentified. For that reason each instance ofanchor text may also be tested for correct navigation to the properresource. If any errors or broken links are found they can be correctedor updated. Instances of anchor text that are not particularlydescriptive of the source that they point to such as “click here” forexample may be recoded to be more descriptive of the actual resource.This process may be used to fine tune inference of intent of visitor'sto the site. If the hierarchies match at step 810 then the system savesand stores the intent tag structure along with the associated Websitenode structure for later reference. The system may then move to a nextcustomer Website. Customer sites may be processed in batchesautomatically.

In the process of visitor monitoring and tracking, the intent tagsmanipulated by the visitor are mapped to paths in the Web tree. The“tags” of anchor text determine the intent of the visitor. The hierarchyof Web pages translates to a hierarchy of tags. The amount of time spentby a visitor on a “tag” equal the amount of time spent on all the tagsin the sub tree under that “tag”. Tag trees from multiple Websitesanalyzed would give us multiple hierarchies of intent. Whenever avisitor visits a monitored Website the tags associated with the visitare mapped to paths in the Website tree. The intent of a visitor isbasically a function of a few things, the tags associated with the visitand the time period associated with each tag; and the path traversed bythe visitor in the tree.

Converting a Website into a Hierarchy

One way to create a hierarchical structure from a Website is to do a“crawl” of the Website. The result of a website crawl is a graph of thewebsite but the graph alone does not yield the hierarchy which isevident to human users upon visiting such a Website. Websites aretypically designed as trees to decrease the time required to access aparticular page. However, further links are added between inter-relatedpages to allow a user to easily access related content. This linkaddition process turns the tree into a graph. This graph is directed andcyclic. Let G(V,E) be such a graph, which represents a website. Everypage on the website is represented by a vertex in the graph. Thereexists an edge from a vertex v1 to another vertex v2, if the page p1represented by vertex v1 contains a hyperlink to page p2, represented byvertex v2. The website root is termed as the root node of G. The tagtree T is inferred from this graph, such that all edges between twovertices in different sub-trees of T are eliminated. However, everygraph has several spanning trees. Only one of these several spanningtrees matches the hierarchical structure perceived by the websitevisitor. To determine which tree matches the to hierarchical structure,the following algorithm is used.

Firstly, the immediate successors of the root node are classified asdifferent buckets. Buckets are sections into which the website can bedivided based on the intent of the visitor, for example, Products,Solutions, and Services, all sections that relate to the items that thecompanies sell. Each bucket is assigned a certain weight based on itsrelative importance to marketing. These weights are represented on theedge from the root node to the immediate successor nodes.

Now for each vertex of G considered in the topologically sorted order:

(a) Determine the overall vertex weight. This weight is determined as afunction of all the incoming edge weights. For example if a vertex v has8 incoming edges, then vertex weight is determined by f(e1, e2, e8).Several functions may be used for f( ) viz. such as average, maximum,minimum etc.(b) Assign an edge weight proportional to g(f( . . . )), where g( ) istypically implemented as a multiplicative dampening factor to theoutgoing edges of v.

Secondly, use a spanning tree generation algorithm to determine theminimal (or maximal) spanning tree. Typical spanning tree algorithmsinclude Edmond/Chu-Liu spanning tree algorithm. In summary and in actualpractice, automatic generation of the spanning tree of the graphobtained after crawling a website is performed by . . . .

(a) Classifying the top level nodes into buckets.(b) Assigning weights to each of the buckets of step (a).(c) Propagating the weights to each of the edges through appropriatevertex weight function and dampening factor functions.(d) Computing the minimal (or maximal) spanning on the graph.

Tags of intent are used as a criterion for clustering visitors intovisitor groups sharing similar intent. The association is determinedusing multiple techniques and sources including word similarity matchingtechniques which measure phrase distances; phrase corpuses for differentinformation domains or industries or knowledge fields; and to phrasestyped in search engines. In one embodiment application programinterfaces (API's) to third-party applications like Bing™ and Google™may be used to map the tags to specific departments, titles, people, andassociated products and services. A person interested in a server wouldbe associated with data center services, firewall, load balancer, etc.

Determining Visitor ID

Users browsing a site can be identified based on activities like formfill, response to an email, logging in to a partner site etc. Once theemail ID is obtained the title of the person can be obtained throughinformation available from publicly accessible sources. Once the titleis determined, the website activity of that visitor may be marked withthe title of the visitor. When a similar activity is observed, then thetitle of the visitor may be predicted based on historical data.Similarity amongst the activity can be measured by either of thefollowing methods:

(1) Each node in the graph is associated with a probability of the nodebeing visited by a person of a certain title. When a person visits awebsite, the probability of the various titles, from all the nodestraversed is determined and then the probability of a certain title isdeduced.(2) Matching all the nodes visited and finding the number of nodesmatching to be greater than a certain threshold. A score may be computedusing a function l(IPnode1, IPnode2, . . . ) where IP is the inversepage rank of the node. In case the scores of both the visits are withina certain threshold, then the visits may be considered to be similar.(3) Matching the aggregate G-score of a previous visit with the G-scoreof the current visit. If these G-scores are within same range, then thetitles are predicted to be the same. The first scheme described abovehas, in a preferred embodiment, a higher priority over the second. Ifthe data for evaluating the first scheme is absent, then the secondscheme may be initiated.

In case contact information is not provided and sufficient historicaldata does not exist to determine the identity of the visitor, then analternate mechanism may be used. Each node of the spanning Tree T may beassociated with a title or designation of the visitor who might showpotential interest. The primary interest of the visitor is determined bypicking the top n pages with the highest G-score. For each of thesepages the title tag is extracted. For each title tag extracted, thecontact information from the visitor's organization may be determinedfrom the contact database. If no information exists then contactinformation may be determined for the parent node in the spanning tree Tand in the absence of which the contact information of its parent nodeand so on.

(1) Determination of title of a visitor may be based on based on emailID using publicly available sources for email ID to title information,where the email ID can be obtained from a plethora of sources such aslogin information, form file, response to an email and the like.(2) Tagging the nodes visited by a visitor whose title has beendetermined, with the title of the visitor.(3) Discovering similarity between two visits by (a) computing a visitscore on all pages visited using a function l(IPnode1, IPnode2, . . . )where IP is the Inverse Page Rank of the node and (b) comparing thevisit scores of two visits and finding it to be close to each otherseparated by not more than a certain threshold.(4) Using the visit similarity to infer the title of the visitor.(5) Using the aggregated G-score of two visits to find the similarity oftwo visits.(6) Tagging the nodes of the website, with potential title of thevisitor.(7) Computing the probability of a certain title having visited thewebsite by aggregating the probabilities of all the pages visited duringthat session, where each page is assigned a probability of visit by avisitor of a certain title visiting. The visitor title probability isobtained from historical data.(8) Sorting the probability of the title of the visitor obtained fromthe aggregating the probabilities.(8) Determining the visitor title information based on the nodes visitedand using the contact database to determine the email IDs in thevisitor's organization, where the visitor's organization is determined.(10) Ordering contact information obtained based on the G-score of thevisited nodes, where the G-score is computed.

There are several unique ways in embodiments of the present invention toidentify visitors. One way is by emailing a visitor, once that visitor'semail address is known, with an invitation to call an agent or an IVRsystem. The telephone number included in the email is a unique number,that is, it is not sent to any other visitor. The telephone number thatthe person uses to dial the agent thus becomes an identifier of thecaller, and completes the association. Another is by including an htmlweblink in an email to the person, wherein a unique identifier isassociated with the weblink sent in an email. If the person invokes thelink in the email, the associated identifier completes the association.Still another means is by using any information determined about theperson to access any number of on-line and otherwise accessible sourcesto add to the information about the person. For example, is the person'semail address is known, that may be used as search criteria to accessand search any number of data resources to determine more information.

Another important and innovative means of identification is byassociation with other visitors to a website. Giving a relatively largenumber of visitors who may each exhibit the same or similar behavior onthe site, if ID and data are known for some of the visitors, it may beinferred that the other visitors, not yet identified are highly likelyto have similar interests, business titles and the like to the visitorswhose identity is known. The inference of like characteristics may beused as clues to further deduce other information about the yet-to-beidentified visitors, leading eventually to identification.

FIG. 9 is a process flow diagram illustrating steps 900 for monitoringand tracking Web visitor mouse movement relative to anchor text for thepurpose of inferring intent of the visitor according to an embodiment ofthe present invention. At step 901 a Web visitor is detected on the rootpage of the Website. At step 902 the system begins tracking the movementof the visitor. In one embodiment the system continues tracking thevisitor if the visitor left and returned to the root page. It is alsonoted herein that a visitor may, in some cases, enter a Website byaccessing a page further down the tree so it is important that thevisitor is detected at any staring point of entry to the Website duringa visit.

At step 903 the system determines if the visitor hovered over any anchortext. If the visitor hovered over an instance of anchor text then thesystem records or captures the event and the time spent hovering overthe text at step 904. The process loops back to step 903 and proceedsagain top step 904 every time the system detects a hover at step 903.This is ongoing during the total time of the visit. If the systemdetermined that the user did not hover on any anchor text at step 903,the system determines at step 905 whether the visitor clicked on anyinstances of anchor text. If the user clicked on anchor text at step905, then at step 906 the system captures the event and begins a timerto time the visitor under that “tag”. The time a user spends under aroot tag (anchor text clicked on) is the total time of the Web visitminus the time before the user clicked on any root tags. If the user didnot click on any anchor text, the system loops back and continues tomonitor the visitor relative to hover and click movements.

At step 907 the system determines if the anchor text clicked on by thevisitor leads the visitor to a new Web page. If the system determinesthat the anchor text clicked on by the user invoked a new Webpage, thenthe process lops back to step 902 and the system continues tracking thevisitor at the subsequent Web page. It is important to note herein thata visitor may click on an instance of anchor text, navigate to a newpage of the site and then suddenly hit the back button on the browser tomove back to the root page.

A user having navigated more than one page of the site may re-enterthose pages already visited by manipulating the navigation history login the visitor's toolbar to return to a selected page without clickingon any anchor text. However such navigation through the Website usingthe back/forward button on the browser or selecting a return page fromthe browser navigation history is detected and the user is continuallytracked until the user exits the Website by terminating the onlineconnection or by clicking on anchor text that leads to a page off site.In this regard having a robust link page may benefit determination ofintent where the visitor selects one of the anchor text instances tonavigate to a linked page.

At step 907 if the system determined that the anchor text the userclicked on does not take the visitor to another Webpage, then the systemmay determine at step 908 if the tracking process for that visitor isfinished. The system may determine that the process is finished if thevisitor leaves the Website and can no longer be tracked or goes offline.The system may determine that the process is finished if the visitor isstill at the Website but no activity has occurred for a threshold periodof time.

If the system determines that the tracking process is finished at step908 then the results of monitoring the user are passed to the inferenceengine for that visitor and that visit at step 910. If the systemdetermines that the visitor landed on another Webpage of the Website,the system may make a determination if the tracking process is finishedat step 909. If the system determines that the tracking process is notfinished for the visitor then the process moves back to step 902 wheremonitoring and tracking the visitor continues. If the system determinesthat the tracking process is finished at step 909, then the raw datacaptured during the visit for that visitor is passed to the inferenceengine as input for inferring the intent of the visitor.

In this example, process steps 900 refer to the visitor interacting withinstances of anchor text which leads the user to another resource of theWeb site or to a linked page offsite. However the tracking system mayalso track visitor interaction with a search engine at the site,interaction with hypertext that leads the visitor to another part of thesame page, images, uploads, downloads, comments posted, form filling,multimedia consumption, contact activities (initiating communicationfrom the site), and other activities that can be tracked. All of thisinformation may be passed to the inference engine to aid in determiningthe intent of the visitor.

In a preferred embodiment of the present invention information gatheredfrom monitoring visitor interaction with anchor text on the Website maybe supplemented with information gathered from a plurality of sourcesincluding information gathered through page tagging of the website;information gathered from logs of the website; information gathered fromcontact databases; information gathered from search engines; andinformation available on other websites.

Such data may include date and time of monitored Web visits; the companythat owns or is related to the IP Address associated with a Web visit bya visitor; company to which the visitor is employed or is affiliatedwith; visitor Location including geographic location latitude andlongitude of the visitor, and the city, state, and country of thevisitor.

Other information that may be collected about the visitor may includename, email addresses, phone numbers, and other contact details of thevisitor if the visitor can be identified without ambiguity. One methodof unambiguously determining the identity of the visitor is throughcorrelation of a previously identified visit where a mapping between theinformation sent by the browser of the visitor is associated with theidentity and contact information of the visitor.

If a visitor cannot be identified without ambiguity then the system maygather names, email addresses, phone numbers, and other contact detailsof possible visitors if there is ambiguity about the identity of thevisitor. This information may be obtained from third party contactdatabases that list people in various companies along with their names,email addresses, location information, and phone numbers. The possiblevisitors are determined by using a plurality of information includingcompany name associated with the visitor, the geographical location ofthe visitor, inferences made about the visitor's department and titlemade from the browsing patterns and the phrases and keywords browsed bythe visitor. The probable set departments and titles of the visitor canalso be determined based on prior knowledge about who usually visits thewebsite that is gathered from a plurality of sources including inputfrom the website owners and operators.

The system uses the visitor's browsing pattern, time spent, clickpattern, and mouse movement data on every page on a monitored website toinfer the keywords and phrases of interest, and the pages of interest tothe visitor. The result of this inference may be further analyzed todetermine the intent of the visitor; the level of intent or engagementof the visitor; whether the visitor is interested in a product family orproduct to category or a solution family or a solution category.

In one embodiment information about a Website or Web page visited priorto arriving at a monitored Website is collected if available includingthe URL of the Website or page, the URL structure, the URL path, anyquery strings, and any search terms used at a search engine if thereferring page is a search engine page. Such information may includepage title, meta-tags, text, hyperlinks, anchor text, hypertext, andnon-text elements on the previous page.

All of the information collected about the visitor may be correlated toinformation previously collected from the same visitor to the same site.The data sets for multiple visitors to the same site might be correlatedto determine which visitors have one or more attribute in common such assuch as affiliation with the same company or visiting from the samegeography or visiting around the same time of day or sharing the sameset of Web pages visited or sharing the same referrer or sharing thesame set of search terms or time spent on an individual Web page ortotal time spent on the website (using correlation information toautomatically classify the anchor text).

In a preferred embodiment all of the data collected about visitorsincluding that of clustered visitors or groups is packaged into one ormore information or lead capsules that may include phrases of interestto the visitors; text and non-text elements which were of interest tothe visitor (combination of hypertext, anchor text in hyperlinks, pagetitle, meta-tags, search terms used, text either visible or invisible onthe referring page, non-text elements either clicked on or hovered on bythe visitor etc.); pages of interest to the visitor; pages visitedduring the visit and all visible and invisible text and non-textelements on all the pages visited along with text and non-text elementsextracted from mouse movement and mouse hovering information on all thepages visited.

The information capsules or “lead” capsules also contain, in preferredembodiments indications of visitor intent; name and contact informationof the visitor (if identified without ambiguity) or names and contactinformation of possible visitors (if there is ambiguity); companyaffiliation, department, title, extension, etc. In one embodimentinformation from a sales or marketing campaign may be collected if thevisitor was referred to a tracked Website through a campaign like anemail campaign, a webinar, a search engine campaign, a tradeshow, or thelike. Historical data may be retained relative to a visitor's pastvisits, data from past visits by visitors of a same organization; datafrom past visits by related companies, and any of the information frompast visitors who may share any of the above elements in common with thevisitor. Statistical analysis may be performed on visitor clusters toinfer intent of the cluster of visitors.

It will be apparent to one with skill in the art that the informationgathering and lead generation system of the invention may be providedusing some or all of the mentioned features and components withoutdeparting from the spirit and scope of the present invention. It willalso be apparent to the skilled artisan that the embodiments describedabove are specific examples of a single broader invention which may havegreater scope than any of the singular descriptions taught. There may bemany alterations made in the descriptions without departing from thespirit and scope of the present invention.

1. A system for inferring intent of visitors to a Website comprising: avisitor-tracking application executing from a digital medium coupled toa server hosting the Website, the server connected to a repositoryadapted to store data about visitor behavior; and an inference enginefor processing the data to infer the intent of visitors; wherein visitorbehavior relative to links is tracked, and intent of a visitor isinferred from one or both, or a combination of analysis of the behaviorand deducing meaning for anchor text of links selected.
 2. The system ofclaim 1 wherein the Website consists of one or more Web pages.
 3. Thesystem of claim 2 wherein the one or more Web pages include one or moreblogs, news articles, or ecommerce pages.
 4. The system of claim 1wherein the visitor behavior recorded includes mouseover or clicking ona link with anchor text.
 5. The system of claim 1 further includingvisitor behavior that includes mousing over and clicking on hypertextwherein such recorded behavior and time associated therewith is used tofine tune the level of visitor intent.
 6. The system of claim 1 furtherincluding one or more application program interfaces (APIs) to one ormore third-party data-gathering and holding services and wherein suchdata if discovered is used to fine tune level of intent of the visitorand to identify the visitor without ambiguity.
 7. The system of claim 1wherein one or more of the anchor text instances point to a multimediapresentation, an interactive form, or a data download or uploadinterface.
 8. The system of claim 6 further including a data miningapplication for navigating to external data sources and acquiring datafrom those external data sources and wherein that data, if discovered,is used to fine tune the level of visitor intent and to identify thevisitor without ambiguity.
 9. The system of claim 8 wherein the dataminer is enabled to mine data from a visitor-subscribed Website using alogin token.
 10. The system of claim 1 wherein the visitor behaviorincludes acts of screen capture and or highlighting or download of textor images and wherein that recorded behavior and the time associatedtherewith is used to fine tune the level of visitor intent.
 11. Thesystem of claim 8 wherein the data about one or more visitors and aboutvisitor behavior including intent is packaged as one or more sales leadspresented in a sales information capsule to potential buyers.
 12. Amethod for inferring intent of visitors to a Website comprising thesteps of: (a) tracking visitor behavior by a visitor-trackingapplication executing from a digital medium coupled to a server hostingthe Website, the server connected to a repository adapted to store dataabout visitor behavior; and (b) inferring intent of visitors from thedata by an inference engine deducing meaning of anchor text of linksselected by a visitor, and/or visitor behavior relative to the links.13. The method of claim 1 wherein the Website consists of one or moreWeb pages.
 14. The method of claim 12 wherein the one or more Web pagesinclude one or more blogs, news articles, or ecommerce pages.
 15. Themethod of claim 12 wherein the visitor behavior recorded includesmouseover or clicking on a link with anchor text.
 16. The method ofclaim 12 further including visitor behavior that includes mousing overand clicking on hypertext wherein such recorded behavior and timeassociated therewith is used to fine tune the level of visitor intent.17. The method of claim 12 further including one or more applicationprogram interfaces (APIs) to one or more third-party data-gathering andholding services and wherein such data if discovered is used to finetune level of intent of the visitor and to identify the visitor withoutambiguity.
 18. The method of claim 12 wherein one or more of the anchortext instances point to a multimedia presentation, an interactive form,or a data download or upload interface.
 19. The method of claim 17further including a data mining application for navigating to externaldata sources and acquiring data from those external data sources andwherein that data, if discovered, is used to fine tune the level ofvisitor intent and to identify the visitor without ambiguity.
 20. Themethod of claim 19 wherein the data miner is enabled to mine data from avisitor-subscribed Website using a login token.
 21. The method of claim12 wherein the visitor behavior includes acts of screen capture and orhighlighting or download of text or images and wherein that recordedbehavior and the time associated therewith is used to fine tune thelevel of visitor intent.
 22. The method of claim 19 wherein the dataabout one or more visitors and about visitor behavior including intentis packaged as one or more sales leads presented in a sales informationcapsule to potential buyers.